The actual challenge data consists of 21 phantoms, arranged into seven groups of gradually increasing difficulty, with each level containing three different phantoms, labeled A, B, and C. As the difficulty level increases, the number of holes increases and their shapes become increasingly complex. Furthermore, the view-angle is reduced as the difficulty level increases, starting with a 90-degree field-of-view at level 1, and reducing by 10 degrees at each increasing level of difficulty. Each target is assigned to a single group, therefore, each target is used only once.
The limited data is then passed as input to the submitted algorithms for assessment of the reconstructions. See code examples for more details.
The targets have been scanned using full-angle tomography, and have been appropriately subsampled to create the challenge data. This enables comparison of the limited-angle reconstruction to the ground truth obtainable from the full-angle data. The ground truth is obtained using the segmentation procedure described in a later section.
Each group is specified in the table below. In this table, angular range specifies the view-angle in the limited-angle data. The view-angles in the challenge data will not all begin from 0 degrees.
The test dataset will be made public by the end of the competition.
The training dataset is available here 
Note: The publicly available data will not be used by the committee for measuring the quality of the algorithms submitted to the challenge. These are reserved for developing the algorithms. We measured some extra data for that. The PSFs are the same as in the categories of the public dataset. However, the targets are slightly different in a way that will be made public only after the deadline.
The targets are homogenous acrylic disc phantoms of 70 mm in diameter, with holes of varying shapes made with a laser cutter. Each disk has a different number of irregular holes in random locations. Figure 1 below shows few examples.
The dataset collected for the HTC2022 challenge consists of two separate sets, with identical experimental setup and settings. One set is provided to the competitors as training set for algorithm development, and the other will be used by the organizers to test the reconstruction algorithms. The test set will be made public after the end of the competition.
The training set consists of five phantoms with full angular data. These are designed to facilitate algorithm development and benchmarking for the challenge itself. Four of the training phantoms contain holes. A fifth training phantom is a solid disc with no holes.
We encourage subsampling these datasets to create limited-data sinograms and comparing the reconstruction results to the ground truth obtainable from the full-data sinograms. Note that the phantoms are not all identically centered. Training data for each difficulty group can be created by subsampling these datasets to create limited-data sinograms that match the angular range of each group of the actual challenge data (See Table 1 below).
Note: As the orientation of CT reconstructions can depend on the tools used, we have included example reconstructions for each of the training phantoms to demonstrate how the reconstructions obtained from the sinograms and the specified geometry should be oriented. These reconstructions have been computed using the filtered back-projection algorithm provided by the ASTRA Toolbox.
We have also included segmentation examples of the reconstructions to demonstrate the desired format for the final competition entries. The segmentation images were obtained by the following steps (using this Python code):
The competitors do not need to follow the above segmentation procedure, and are encouraged to explore various segmentation techniques for the limited-angle reconstructions.
The competitors are encouraged to generate extra training data using simulations. The organizing committee will not provide the code to generate new targets before the end of the competition.
The test set will be made public after the end of the competition.
| Group | Angular range | Angular increment | Number of projections |
|---|---|---|---|
| 1 | 90° | 0.5° | 181 |
| 2 | 80° | 0.5° | 161 |
| 3 | 70° | 0.5° | 141 |
| 4 | 60° | 0.5° | 121 |
| 5 | 50° | 0.5° | 101 |
| 6 | 40° | 0.5° | 81 |
| 7 | 30° | 0.5° | 61 |
The dataset is shared using the MATLAB .mat files (version 7.3). Each individual measurement dataset consists of data structure containing the sinogram and its associated metadata, including measurement geometry. In other words, each file contains the measurements for one tomographic image.
Python users can load this type of file into their code using the mat73 module. Please refer to this link on how to install and use the module.
The challenge data was measured at the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The measurement device is a cone-beam computed tomography scanner designed and constructed in-house. The scanner consists of an X-ray source, a rotating sample holder, and an X-ray detector (Figure 2).
The data has already been pre-processed with background and flat-field corrections, and compensated for a slightly misaligned center of rotation in the cone-beam computed tomography scanner. The log-transforms from intensity measurements to attenuation data have also been already computed.
We highlight the following geometric definitions, needed to properly specify the X-ray projection operator for the measurement setup:
The X-ray detector data was binned by a factor of four after the measurements, giving a pixel size of 0.2 millimeters.
